“第十八章 深度学习前沿”版本间的差异

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==高级读者==
 
==高级读者==
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* He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778.[https://openaccess.thecvf.com/content_cvpr_2016/papers/He_Deep_Residual_Learning_CVPR_2016_paper.pdf]
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* Bengio Y, Ducharme R, Vincent P. A neural probabilistic language model[J]. Advances in neural information processing systems, 2000, 13. [https://proceedings.neurips.cc/paper/2000/file/728f206c2a01bf572b5940d7d9a8fa4c-Paper.pdf]
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* Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space[J]. arXiv preprint arXiv:1301.3781, 2013. [https://arxiv.org/pdf/1301.3781.pdf%C3%AC%E2%80%94%20%C3%AC%E2%80%9E%C5%93]
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* Schroff F, Kalenichenko D, Philbin J. Facenet: A unified embedding for face recognition and clustering[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 815-823. [https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Schroff_FaceNet_A_Unified_2015_CVPR_paper.pdf]
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* Li C, Ma X, Jiang B, et al. Deep speaker: an end-to-end neural speaker embedding system[J]. arXiv preprint arXiv:1705.02304, 2017. [https://arxiv.org/pdf/1705.02304]
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* Lin Y, Liu Z, Sun M, et al. Learning entity and relation embeddings for knowledge graph completion[C]//Twenty-ninth AAAI conference on artificial intelligence. 2015. [https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/viewFile/9571/9523]
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* Sutskever I, Vinyals O, Le Q V. Sequence to sequence learning with neural networks[J]. Advances in neural information processing systems, 2014, 27. [https://proceedings.neurips.cc/paper/2014/file/a14ac55a4f27472c5d894ec1c3c743d2-Paper.pdf]
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* Liu Y, Liu D, Lv J, et al. Generating Chinese poetry from images via concrete and abstract information[C]//2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020: 1-8. [https://arxiv.org/pdf/2003.10773.pdf]
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* Sun D, Ren T, Li C, et al. Learning to write stylized chinese characters by reading a handful of examples[J]. arXiv preprint arXiv:1712.06424, 2017. [https://arxiv.org/pdf/1712.06424]
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* Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate[J]. arXiv preprint arXiv:1409.0473, 2014. [https://arxiv.org/pdf/1409.0473.pdf?utm_source=ColumnsChannel]
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* Xu K, Ba J, Kiros R, et al. Show, attend and tell: Neural image caption generation with visual attention[C]//International conference on machine learning. PMLR, 2015: 2048-2057. [http://proceedings.mlr.press/v37/xuc15.pdf]
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* Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[J]. Advances in neural information processing systems, 2017, 30. [https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf]
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* Liu X, Zhang F, Hou Z, et al. Self-supervised learning: Generative or contrastive[J]. IEEE Transactions on Knowledge and Data Engineering, 2021. [https://arxiv.org/pdf/2006.08218]
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* Schneider S, Baevski A, Collobert R, et al. wav2vec: Unsupervised pre-training for speech recognition[J]. arXiv preprint arXiv:1904.05862, 2019. [https://arxiv.org/pdf/1904.05862]
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* Noroozi M, Favaro P. Unsupervised learning of visual representations by solving jigsaw puzzles[C]//European conference on computer vision. Springer, Cham, 2016: 69-84. [https://arxiv.org/pdf/1603.09246.pdf]
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* Devlin J, Chang M W, Lee K, et al. Bert: Pre-training of deep bidirectional transformers for language understanding[J]. arXiv preprint arXiv:1810.04805, 2018. [https://arxiv.org/pdf/1810.04805.pdf&usg=ALkJrhhzxlCL6yTht2BRmH9atgvKFxHsxQ]
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* Brown T, Mann B, Ryder N, et al. Language models are few-shot learners[J]. Advances in neural information processing systems, 2020, 33: 1877-1901. [https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf]
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* Radford A, Wu J, Child R, et al. Language models are unsupervised multitask learners[J]. OpenAI blog, 2019, 1(8): 9.[]
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* Ethayarajh K. How contextual are contextualized word representations? comparing the geometry of BERT, ELMo, and GPT-2 embeddings[J]. arXiv preprint arXiv:1909.00512, 2019. [https://arxiv.org/pdf/1909.00512]
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* Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets[J]. Advances in neural information processing systems, 2014, 27. [https://proceedings.neurips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf]
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* Kingma D P, Welling M. Auto-encoding variational bayes[J]. arXiv preprint arXiv:1312.6114, 2013. [https://arxiv.org/pdf/1312.6114.pdf]
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* 悟道 [http://keg.cs.tsinghua.edu.cn/jietang/publications/ccl2021-wudao-pretrain%20the%20world.pdf]
 
* 悟道 [http://keg.cs.tsinghua.edu.cn/jietang/publications/ccl2021-wudao-pretrain%20the%20world.pdf]
 
* 王东,机器学习导论,第三章,神经模型,2021,清华大学出版社 [http://mlbook.cslt.org]
 
* 王东,机器学习导论,第三章,神经模型,2021,清华大学出版社 [http://mlbook.cslt.org]
 
* Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep Learning [https://www.deeplearningbook.org/]
 
* Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep Learning [https://www.deeplearningbook.org/]

2022年8月6日 (六) 10:04的版本


教学资料

扩展阅读

视频展示

演示链接

  • HoggingFace 演示[1]
  • Quick style transfer [2]
  • Pix2Pix[3]
  • AutoWriter[4]

开发者资源

高级读者

  • He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778.[5]
  • Bengio Y, Ducharme R, Vincent P. A neural probabilistic language model[J]. Advances in neural information processing systems, 2000, 13. [6]
  • Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space[J]. arXiv preprint arXiv:1301.3781, 2013. [7]
  • Schroff F, Kalenichenko D, Philbin J. Facenet: A unified embedding for face recognition and clustering[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 815-823. [8]
  • Li C, Ma X, Jiang B, et al. Deep speaker: an end-to-end neural speaker embedding system[J]. arXiv preprint arXiv:1705.02304, 2017. [9]
  • Lin Y, Liu Z, Sun M, et al. Learning entity and relation embeddings for knowledge graph completion[C]//Twenty-ninth AAAI conference on artificial intelligence. 2015. [10]
  • Sutskever I, Vinyals O, Le Q V. Sequence to sequence learning with neural networks[J]. Advances in neural information processing systems, 2014, 27. [11]
  • Liu Y, Liu D, Lv J, et al. Generating Chinese poetry from images via concrete and abstract information[C]//2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020: 1-8. [12]
  • Sun D, Ren T, Li C, et al. Learning to write stylized chinese characters by reading a handful of examples[J]. arXiv preprint arXiv:1712.06424, 2017. [13]
  • Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate[J]. arXiv preprint arXiv:1409.0473, 2014. [14]
  • Xu K, Ba J, Kiros R, et al. Show, attend and tell: Neural image caption generation with visual attention[C]//International conference on machine learning. PMLR, 2015: 2048-2057. [15]
  • Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[J]. Advances in neural information processing systems, 2017, 30. [16]
  • Liu X, Zhang F, Hou Z, et al. Self-supervised learning: Generative or contrastive[J]. IEEE Transactions on Knowledge and Data Engineering, 2021. [17]
  • Schneider S, Baevski A, Collobert R, et al. wav2vec: Unsupervised pre-training for speech recognition[J]. arXiv preprint arXiv:1904.05862, 2019. [18]
  • Noroozi M, Favaro P. Unsupervised learning of visual representations by solving jigsaw puzzles[C]//European conference on computer vision. Springer, Cham, 2016: 69-84. [19]
  • Devlin J, Chang M W, Lee K, et al. Bert: Pre-training of deep bidirectional transformers for language understanding[J]. arXiv preprint arXiv:1810.04805, 2018. [20]
  • Brown T, Mann B, Ryder N, et al. Language models are few-shot learners[J]. Advances in neural information processing systems, 2020, 33: 1877-1901. [21]
  • Radford A, Wu J, Child R, et al. Language models are unsupervised multitask learners[J]. OpenAI blog, 2019, 1(8): 9.[]
  • Ethayarajh K. How contextual are contextualized word representations? comparing the geometry of BERT, ELMo, and GPT-2 embeddings[J]. arXiv preprint arXiv:1909.00512, 2019. [22]
  • Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets[J]. Advances in neural information processing systems, 2014, 27. [23]
  • Kingma D P, Welling M. Auto-encoding variational bayes[J]. arXiv preprint arXiv:1312.6114, 2013. [24]


  • 悟道 [25]
  • 王东,机器学习导论,第三章,神经模型,2021,清华大学出版社 [26]
  • Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep Learning [27]